• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.371-382
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• 2005

We present a processing system for the Atmospheric Infrared Sounder (AIRS) sounding suite onboard Aqua satellite. With its unprecedented 2378 channels in IR bands, AIRS aims at achieving the sounding accuracy of radiosonde (1 K in 1-km layer for temperature and $10\%$ in 2-km layer for humidity). The core of the processor is the International MODIS/AIRS Processing Package (IMAPP) that performs the geometric and radiometric correction for generation of Level 1 brightness temperature and Level 2 geophysical parameters retrieval. The processor can produce automatically from received raw data to Level 2 geophysical parameters. As we process the direct-broadcast data almost for the first time among the AIRS direct-broadcast community, a special attention is paid to understand and verify the Level 2 products. This processor includes sub-systems, that is, the near real time validation system which made the comparison results with in-situ measurement data, and standard digital information system which carry out the data format conversion into GRIdded Binary II (GRIB II) standard format to promote active data communication between meteorological societies. This processing system is planned to encourage the application of geophysical parameters observed by AIRS to research the aqua cycle in the Korean peninsula.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.74-79
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• 2017

The Geostationary Ocean Color Imager (GOCI), the world's first ocean color sensor operated in a geostationary orbit, can be utilized to mitigate damages by monitoring marine disasters in real time such as red tides, green algae, sargassum, cold pools, typhoons, and so on. In this paper, we described a methodology and procedure for processing GOCI data in order to maximize its utilization potential. The GOCI data processing procedure is divided into data reception, data processing, and data distribution. The kinds of GOCI data are classified as raw, level 1, and level 2. "Raw" refers to an unstructured data type immediately generated after reception by satellite communications. Level 1 is defined as a radiance data type of two dimensions, generated after radiometric and geometric corrections for raw data. Level 2 indicates an ocean color data type from level-1 data using ocean color algorithms.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1529-1539
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• 2023

The data from Geostationary Ocean Color Imager-II (GOCI-II), which observes the color of the sea to monitor marine environments, undergoes various correction processes in the ground station system, producing data from Raw to Level-2 (L2). Quality issues arising at each processing stage accumulate step by step, leading to an amplification of errors in the satellite data. To address this, improvements were made to the GOCI-II ground station system to measure potential optical quality and geolocation accuracy errors in the Level-1A/B (L1A/B) data. A newly established Radiometric and Geometric Performance Assessment Module (RGPAM) now measures five optical quality factors and four geolocation accuracy factors in near real-time. Testing with GOCI-II data has shown that RGPAM's functions, including data processing, display and download of measurement results, work well. The performance metrics obtained through RGPAM are expected to serve as foundational data for real-time radiometric correction model enhancements, assessment of L1 data quality consistency, and the development of reprocessing strategies to address identified issues related to the GOCI-II detector's sensitivity degradation.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.41-48
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• 2012

Using multi-sensor or multi-temporal high resolution satellite images together is essential for efficient applications in remote sensing area. The purpose of this paper is to estimate geometric difference of translations between high-resolution optical and SAR images automatically. The geometric and radiometric pre-processing steps were fulfilled to calculate the similarity between optical and SAR images by using Mutual Information method. The coarsest-level pyramid images of each sensor constructed by gaussian pyramid method were generated to estimate the initial translation difference of the x, y directions for calculation efficiency. The precise geometric difference of translations was able to be estimated by applying this method from coarsest-level pyramid image to original image in order. Yet even when considered only translation between optical and SAR images, the proposed method showed RMSE lower than 5m in all study sites.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.29-34
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• 2002

In this paper, we present a new geometric active contour model based on level set methods introduced by Osher and Sethian for detection of object boundaries or shape and we adopt anisotropic diffusion filtering method for removing noise from original image. In order to minimize the processing time, we use the narrow band method which allows us to perform calculations in the neighborhood of the contour and not in the whole image. Using anisotropic diffusion filtering for each slice, we have the result with reduced noise and extracted exact shape. Volume rendering operates on three-dimensional data, processes it, and transforms it into a simple two-dimensional image.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.3B
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• pp.557-564
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• 2000

This paper presents a watermarking algorithm for embedding visually recognizable pattern (Mark, Logo, Symbol, stamping or signature) into the image. first, the color image(RGB model)is transformed in YCbCr model and then the Y component is transformed into 3-level wavelet transform. Next, the values are assembled with pattern watermark. PN(pseudo noise) code at spread spectrum communication method and mutilevel watermark weights. This values are inserted into discrete wavelet domain. In our scheme, new calculating method is designed to calculate wavelet transform with integer value in considering the quantization error. and we used the color conversion with fixed-point arithmetic to be easy to make the hardware hereafter. Also, we made the new solution using mutilevel threshold to robust to common signal distortions and malicious attack, and to enhance quality of image in considering the human visual system. the experimental results showed that the proposed watermarking algorithm was superior to other similar water marking algorithm. We showed what it was robust to common signal processing and geometric transform such as brightness. contrast, filtering. scaling. JPEG lossy compression and geometric deformation.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.791-802
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• 2023

The manual inspection of photovoltaic (PV) panels to meet the requirements of inspection work for large-scale PV power plants is challenging. We present a hot spot detection and positioning method to detect hot spots in batches and locate their latitudes and longitudes. First, a network based on the YOLOv3 architecture was utilized to identify hot spots. The innovation is to modify the RU_1 unit in the YOLOv3 model for hot spot detection in the far field of view and add a neural network residual unit for fusion. In addition, because of the misidentification problem in the infrared images of the solar PV panels, the DeepLab v3+ model was adopted to segment the PV panels to filter out the misidentification caused by bright spots on the ground. Finally, the latitude and longitude of the hot spot are calculated according to the geometric positioning method utilizing known information such as the drone's yaw angle, shooting height, and lens field-of-view. The experimental results indicate that the hot spot recognition rate accuracy is above 98%. When keeping the drone 25 m off the ground, the hot spot positioning error is at the decimeter level.

• ETRI Journal
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• v.38 no.5
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• pp.868-878
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• 2016

A multi-screen environment provides a new opportunity for digital signage applications, where various irregularly shaped screens are employed as an effective means for advertisements and information. A formation method applicable to numerous irregularly shaped screens is proposed to provide a new perspective on digital signage applications. The crucial part of the proposed method comes from the geometric sensing of each screen and the formation of a virtual screen where geometrically aligned content extraction and encoding are employed for content transmission to each screen. In addition, a software-based synchronization method for the proposed system is proposed to address the frame-level synchronization between screens. The experimental results of the proposed method show an improved performance of the frame-level synchronization, where the inconsistency between frames is not identified.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.375-377
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• 2008

The KOMPSAT2 Terminal Polar Station was recently installed at near North Pole, Tromso, and Toulouse. The K2PS consists of one receiving station and two processing sites. The receiving station has been installed at SvalSat ($N78^{\circ}$, $E15^{\circ}$ ), and the two receiving sites have been installed at KSAT (Kongsberg Satellite Service AS), Tromso, Norway ($N69^{\circ}$ ,$E18^{\circ}$ ) and SISA, Toulouse, France ($N43^{\circ}$ ,$E1^{\circ}$ ). The products ofK2PS system can be classified to two categories: Level 1R product and Level 1G product. The Level 1R product is radiometric corrected product with RPC (Rational Polynomial Coefficients) and the Level 1G product is geometric corrected product with POD (Precise Orbit Data) and PAD (Precise Attitude Data) data based on Level 1R product. To meet a SISA (Spot Image SA)'s requirement, K2PS system has high performance product producing capability. This paper describes overall K2PS systems' production generation flow and the mass production test result of K2PS systems.

• Journal of the Korean Wood Science and Technology
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• v.26 no.2
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• pp.29-37
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• 1998

An image analysis program with the function of measuring various quantitative characteristics in the transverse surface of wood was developed using Delphi 2.0. Data on pore characteristics (conditions for image processing, proportion of pores in relationship to other elements, tangential diameter, area, tangential and radial diameter, x and y coordinates of pore center, and geometric coefficients) were saved in text file format. In addition, the pore area histogram in the tangential and radial directions was saved as a BMP (bitmap) type file. Analyses indicated that quantitative characteristics such as the relative radial distribution of pores in a growth ring, pore tangential area histogram, and proportion of pore in lumen area appear to be useful in separating four diffuse-porous woods and four ring-porous woods on the species level.